![]() However, the complexity of mining geological conditions, material strength, and human factors have a significant influence on the experimental results. This method has major advantages, such as intuition, simplicity, and short experimental period. A formation model, resembling the actual project, is constructed in the laboratory, according to the similarity principle, and the predicted parameters are inferred by monitoring the changes of the model. Similarity simulation is an effective method to acquire predicted parameters. Moreover, the obtained parameters are only applicable to working faces under similar geological conditions, and the scope of application is limited. However, an accurate measurement is very time-consuming and requires a long period (at least 1 or 2 years), which squanders a lot of manpower and material resources. The excellent correlation between the modelled and measured data documents that our method provides, demonstrated a new efficient and valuable tool for the precise prediction of damages induced by mining of underground coal seams in loess donga.įield measurements are a common method to obtain detailed and reliable predicted surface parameters. The calculated error of the additional displacement of slope mining slip is between 1.0–9.8%. Our results show that the agreement between the curves predicted from our calculations and from the measured data are between 88.7–97.8%. Finally, combined with the subsidence prediction results of the strata area and the slope sub-area, and the position of the slope, the accurate prediction of the surface subsidence in loess donga is realized. Secondly, according to the slope stability and slip principle, the additional displacement of subsidence in the slope area with mining instability coefficient G cs > 0.87 is calculated. First, based on the theory of probability integral and rock formation movement, the probability integral parameters of the horizontal stratum area are determined, and the subsidence basins in the area are superimposed and calculated. A method for predicting the subsidence value of two regions is established. The prediction idea of “dividing the surface of loess donga into horizontal strata area and slope sub-area, and predicting the subsidence value of the two areas, respectively” is put forward. ![]() The results show that slope slippage is the source of error in the prediction of subsidence in loess donga. ![]() ![]() The feasibility of our new method was verified by a case study in the N1114 working face of the Ningtiaota coal mine (China) that is situated in an area with abundant loess dongas. Therefore, we propose a method for predicting surface subsidence of coal seam mining in loess donga that is based on the probability integration model, combined with the movement principle of rock and soil layers in the respective study area, and considering the influence of slope stability and additional mining slip on mining subsidence. However, conventional models are very problematic, and the reliability of prediction is usually low. The accurate prediction of surface subsidence is a significant foundation for the damage assessment of coal seam mining and ecological environment reclamation in loess donga. ![]()
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